Assessing complexity of dialogs to streamline handling of service requests

ABSTRACT

A dialogue complexity assessment method, system, and computer program product for introducing the notion of dialogue complexity to understand and compare dialogues in a repository, calculating the dialogue complexity, use the dialogue complexity to understand customer interactions in a variety of domains using public and proprietary data, and demonstrate the dialogue complexity usage to improve a service management operation.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation Application of U.S. patentapplication Ser. No. 15/582,096, filed on Apr. 28, 2017, the entirecontents of which are hereby incorporated by reference.

BACKGROUND

The present invention relates generally to a dialogue complexityassessment method, and more particularly, but not by way of limitation,to a system, method, and computer program product for determiningcomplexity as a data-driven, context-independent indicator to managesets of dialogs and services operations.

Service industry thrives on engaged customers using a company'sofferings, and dialogs, whether written or spoken, is a common form ofsuch an interaction. Over time, organizations collect a sizable volumeof dialogue data that may be proprietary or public depending on howcustomer service is provided.

As a customer calls up their service provider for a request, theirinteraction may be routine or extra-ordinary. Recently, there has beensignificant interest in the service management domain to automaticallyanalyze such interaction data to better understand customer needs andways to address them. For example, conventional techniques haveconsidered tracking high-level indicators such as sentiments about howcustomer interactions are progressing in a service center and enablemanagers to take pro-active actions.

Thus, there is a need in the art for a dialogue complexity measure tocharacterize interactions with customers at the levels of utterances,turns and overall dialogs using dialogue data from online repositoriesas well as contact centers of service providers.

SUMMARY

In an exemplary embodiment, the present invention can provide acomputer-implemented dialogue complexity assessment method, the methodincluding calculating staged measures of a complexity of a dialogue by:computing the complexity of the dialogue at an utterance level,computing the complexity of the dialogue at a turn level, and using thetwo complexity measures to compute the aggregate complexity of thedialogue. One or more other exemplary embodiments include a computerprogram product and a system.

Other details and embodiments of the invention will be described below,so that the present contribution to the art can be better appreciated.Nonetheless, the invention is not limited in its application to suchdetails, phraseology, terminology, illustrations and/or arrangements setforth in the description or shown in the drawings. Rather, the inventionis capable of embodiments in addition to those described and of beingpracticed and carried out in various ways and should not be regarded aslimiting.

As such, those skilled in the art will appreciate that the conceptionupon which this disclosure is based may readily be utilized as a basisfor the designing of other structures, methods and systems for carryingout the several purposes of the present invention. It is important,therefore, that the claims be regarded as including such equivalentconstructions insofar as they do not depart from the spirit and scope ofthe present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the invention will be better understood from the followingdetailed description of the exemplary embodiments of the invention withreference to the drawings, in which:

FIG. 1 exemplarily shows a high-level flow chart for a dialoguecomplexity assessment method 100 according to an embodiment of thepresent invention;

FIG. 2 exemplarily depicts a distribution of turn complexity in step 101according to an embodiment of the present invention;

FIG. 3 exemplarily depicts an adaptive system architecture according toan embodiment of the present invention;

FIG. 4. exemplarily depicts ground truth acquisition according to anembodiment of the present invention;

FIG. 5 depicts a cloud-computing node 10 according to an embodiment ofthe present invention;

FIG. 6 depicts a cloud-computing environment 50 according to anembodiment of the present invention; and

FIG. 7 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The invention will now be described with reference to FIGS. 1-7, inwhich like reference numerals refer to like parts throughout. It isemphasized that, according to common practice, the various features ofthe drawings are not necessarily to scale. On the contrary, thedimensions of the various features can be arbitrarily expanded orreduced for clarity.

By way of introduction of the example depicted in FIG. 1, an embodimentof a dialogue complexity assessment method 100 according to the presentinvention can include various steps for calculating a complexity of adialogue, determining a reason for the complexity, and manage a servicehandling.

By way of introduction of the example depicted in FIG. 5, one or morecomputers of a computer system 12 according to an embodiment of thepresent invention can include a memory 28 having instructions stored ina storage system to perform the steps of FIG. 1.

Although one or more embodiments may be implemented in a cloudenvironment 50 (see e.g., FIG. 6), it is nonetheless understood that thepresent invention can be implemented outside of the cloud environment.

With reference generally to FIGS. 1-4, dialogue is made up of a seriesof turns, where each turn is a series of utterances by one or moreparticipants playing one or more roles. In the example of customersupport center, a user contacts a service center and enters into adialogue with a customer support agent. The participant roles here arethat of a customer and an agent, and the roles inter-leave in everyturn. On the other hand, in the example of online support, a person mayraise an issue on a public portal and anyone may reply. The role of allparticipants here is that of a portal user because the original userrequest may remain unresolved. Each user utterance in such a case ofsingle role to define a new turn.

Referring now to FIG. 1, in step 101, staged measures of a complexity ofa dialogue are calculate. That is, the complexity of the dialogue iscalculated by computing the complexity at an utterance level, at a turnlevel, and then at a dialogue level based on the turn level andutterance level complexity.

The desiderata from a dialogue complexity measure are that it can beautomatically calculated, can be agnostic to representation (e.g.,attributes, values, entities) and yet be able to incorporate them whereavailable, can allow comparison of dialogues, be easy to interpretsource of complexity, be compassable over dialogue structure to allowcomputation ease and any relative weighing, and support boundarycondition properties.

The boundary conditions are complexity of an utterance with lessparticipants should be less than or equal to the same utterance withmore participants and complexity of an utterance with less complex wordsshould be less than or equal to the same utterance with more complexwords.

The complexity of the utterance level is computed where a word phrasew_(i), denoted c(w_(i)), is defined by following terms calculated in thegiven order:

$\begin{matrix}{{c\left( w_{i} \right)} = \left\{ \begin{matrix}1 & {\left| {w_{i} \in {DS}} \right.\mspace{14mu}} \\0.5 & {\left| {w_{i} \in {ES}} \right.\mspace{14mu}} \\0 & \left| {w_{i} \in {SWL}} \right.\end{matrix} \right.} & (1)\end{matrix}$

where SWL represent the set of stop words, ES stand for the set ofEnglish subset (common words), DS for domain specific words/phrases andrest of the words are part of noise set NS. An utterance U consists ofword phrases w_(i) such that |U|=N_(U) ^(ω)=Σ₁ ^(|U|)ψ_(i).

The complexity of an utterance, denoted c(U), is defined as:

$\begin{matrix}{{c(U)} = \frac{\Sigma_{i = 1}^{|U|}{c\left( w_{i} \right)}}{|U|}} & (2)\end{matrix}$

A turn is a collection of utterances where each role gets to speak atleast once. For a 2-role dialogue, a turn consists of two utterances.Two equations (3) and (4) of turn complexity are proposed. The first oneis averaging utterances within a turn, calculated by:

$\begin{matrix}{{c(T)} = \frac{\Sigma_{i = 1}^{|T|}{c\left( U_{i} \right)}}{|T|}} & (3)\end{matrix}$

where the number of utterances U_(i) within the turn T is denoted by|T|. Since turn complexity can be seen as a way to reflect thecomplexity of interactions at the moment of the turn, in an-otherdefinition, dialogue acts tag to calculate a weighted sum of utterancecomplexity. Dialog acts are tags that indicate the communicativefunction of the utterance. For example, an utterance may intend forrequesting information, providing information, or for social functionssuch as greetings or closing the dialogue. Dialog act can be bothmanually or automatically tagged.

The weighted turn complexity is calculated by:

$\begin{matrix}{{c\left( T_{DA} \right)} = \frac{\Sigma_{i = 1}^{|T|}{c\left( U_{i} \right)}*w^{\alpha {(U_{i})}}}{|T|}} & (4)\end{matrix}$

where a function α(U_(i)) is available to get the dialogue act tag forutterance U_(i). Further, for each dialogue act j, its weight is denotedby w_(j) (in 0-1 range). The utterance and turn complexity measuresdefined above look at the content of interaction. To measure complexityat a dialogue level, both the content and its structure are allowed tobe considered. Thus, two components are available in the calculation:average turn complexity to reflect the content complexity, and thelength of the dialogue relative to the maximum length in the dialoguedataset of that kind. The latter component can be seen as reflecting thestructural complexity (length) of the particular dialogue relative tothe maximum structural complexity (length) that the service contextallows. While the dialogue length is used as a simple indicator, moresophisticated structural features can be introduced. One can weigh theseindependent components to arrive at the total dialogue complexity.

$\begin{matrix}{c_{D} = {{c(D)} = {{w_{1}*\frac{\Sigma_{i = 1}^{N{(t)}}{c\left( T_{i} \right)}}{N_{D}^{T}}} + {w_{2}*\frac{N_{D}^{T}}{N_{D}^{T_{\max}}}}}}} & (5)\end{matrix}$

where the number of turns T_(i) in the dialogue D by |D|=N_(D) ^(T). LetN_(D) ^(T) ^(max) be the maximum number of turns per dialogue in thedataset S (D_(i)∈S).

If w₂=0 is used, content is only considered. However, in embodiments ofthe invention, equal weight to both with w1=w2=0.5.

Thereby, the overall dialogue complexity can be calculated byaggregating turn complexities by, for example, using a weighted sum ofturn complexities, using a weighted decay based on estimated length ofdialog, and using machine learning based methods such as supervisedlearning using human-annotation of dialogue complexity and predictingdialogue length, decay parameter, etc.

Thus, the proposed metric is compositional, and uses available dialoguecontent and structure. In traditional analysis of dialogs fromlinguistics point of view, the focus is on read-ability of dialogs. Thedisclosed complexity measure focuses on word selection and themeaning(s) they may convey. One can conceive more advanced metrics suchas by estimating the ability of a person to use the dialogue to performa particular task better, provided additional data is availableconveying signals about goodness of task accomplished.

It is noted that the complexity calculation makes distinction betweendomain specific terms, common language (English) terms and stop words,that do not convey significant meaning. When analyzing utterances, themethod can use single words, and in other embodiments, multi-wordphrases or more generally, N-gram structure, i.e., a sliding window ofN-neighboring words.

Dialog data is not uniform and so is the exhibited complexity. In FIG.2, analysis on three dialog datasets is shown: Human Resources (HR),Restaurant booking and Ubuntu online technical support. We see thatdialogues for Ubuntu technical support have lower average turncomplexity. By examining random sample of dialogues, the reason can bedetermined as speakers' lower domain expertise in this case comparing toother more familiar topics. Dialogues of a Human Resource agent has morepolarized distribution, with highest percentage at the low end ofcomplexity. By examining dialogues in with low complexity, the reasoncan be determined as more frequent social chit-chat with the HumanResource agent.

In step 102, a reason for the complexity of the dialogue is determined.That is, rule-based interpretation of complexity differences or changesfor underlying reasons. Possible rules include, for example, complexitydue to language usage (i.e., high average utterance complexity due todomain expertise), complexity due to procedural structure (i.e., highaverage turn complexity due to intensive information requests (insteadof chit-chat)), and complexity due to inherent domain difference (i.e.,high average dialog complexity in a medical domain).

Thus, the reason for calculated values of dialog complexity isexplainable using rules, where the rules cover language; structure ofdialog in terms of constituent turns, utterances and words; domain ofconversation, and understandability consideration like inference chain.

In step 103, a service handling is managed based on the measuredcomplexity of the dialogue and the reason for the complexity of thedialogue. For example, choice of handlers, system modules, andrepository can be decided. That is, in step 103, service handling flowis managed based on dialog complexity, performance of service agents canbe assessed, and system components can be selected based on complexity.

For example, a customer support center can have M agents. An agent a_(j)handles Na_(j) dialogs in time Ta_(j). A function φ(di) is given to findthe customer's satisfaction (C-SAT) with a dialog d_(i) and itscomplexity is measured by function c(d_(i)). The problem is to assessthe performance of a support center's agent, represented as ω(a_(j)).Thus, ω(a_(j)) is defined by Equation 6. Here, the customer rating of aninteraction is weighted with its complexity and duration, and averagedover the whole duration that an agent has to be evaluated. The result isa number which will be between 0-1 if c and C-SAT are in that range. Noweven agents who work over different time periods (Ti) and nature ofdialogs can be compared.

$\begin{matrix}{{\omega_{3}\left( a_{j} \right)} = {\frac{1}{T_{\alpha_{j}}}*\left( {\sum\limits_{i = 1}^{N_{\alpha_{j}}}\; {{c\left( d_{i} \right)}*{\varphi \left( d_{i} \right)}*t_{i}}} \right)}} & (6)\end{matrix}$

Thereby, step 103 can provide a technique to handle a service request ateach turn based on dialog complexity, to rank a set of service handlersbased on dialog complexity, to rank dialogs based on dialog complexity,and to enhance and improve ground truth acquisition for a dialog system.

For example, a handler model can be provided for a turn in which anautomated handler has a value of 1 with a cost of 0.7 and a success rateof (if partial dialogue complexity <0.5), 0.9; else 0.1 and a human hasa value of 1, a cost of 1, and a success rate of 1. In other words, theautomated response has a high success rate when the complexity of thedialogue is low. In step 103, the balance between the automated responseand the human response is computed to maximize the return function. Inother words, an optimization problem can be set up where MaximizeΣ_(Expected remainder dialog turns i) Expect(Value_(i)−Cost_(i)).

FIG. 3 shows an application of dialog complexity metric for managingdialogs that a system has with a person. In this example, the system hasa number of alternative components at each stage of processing: dialoginterface, dialog understanding and dialog management. A computationaldialogue system can determine which modules to be used for a user or fora task based on the complexity of dialogues a user is conducting. Theestimation can be made from either real-time incoming dialogues, orhistorical data of the user or the task. When the dialogue complexityreaches certain threshold, it can be determined that certain costlymodules should be used. When below the threshold, these modules may beexcluded to balance cost. The cost may include, but should not belimited to, development cost and running cost. The more costly modulesmay include, but should not be limited to, semantic parsing inunderstanding user input, inference engine for dialogue management, andmixed-initiative interaction in dialogue system interface.

FIG. 4 shows another application of dialog complexity metric, this timeto help a dialog system built using machine learning. Most such dialogsystems that utilize machine learning techniques are supervised. Animportant task of supervision is annotation of utterances to train amachine learning model (conversation model). The annotation is performedby domain experts by mapping/labeling utterances from users toappropriate responses. However, selecting utterances to be labeled froma large pool of unlabeled utterances is critical to build an accurateconversational model. A prominent technique for selecting data pointsand re-training the machine learning models, that includesconversational models, is Active Learning. In such scenarios, in oneembodiment, the complexity measure in conjunction with other featuressuch as confidence (the probability of machine learning classifier toclassify the utterance) can be used to select utterances from unlabeledset to be annotated. The selection process will now have the ability tolearn the range of complexity scores for utterances to be labeled toimprove the performance of the conversation model.

Thus, the method 100 can directly use a dialogue corpus to automaticallylearn (extract) domain specific terms and uses them along with language(English) terms and keywords for complexity calculation. This makes thecalculation an automatic process without need for input content markers.Also, the method 100 can generate complexity scores at multiple levelsof dialog to facilitate interpretations of complexity and enablecorrespondence in system development and in addition to content-basedcalculation, the invention can also calculate complexity based onmachine learning methods, by taking user utterance as input, assuminglabeled data is available. The invention uses rules to explain reasonfor dialog complexity, where the rules cover language of dialog, domainof conversation, and understandability of content measured by theiruniqueness to domain. Also, the invention can measure complexity basedon variance in volume of content, date and time of content recording andits applicability, sentiment of participants (human or agents).

The embodiment of the proposed method was implemented and run on publicdialog datasets. The average calculated complexity for them at differentlevels are shown below. It helps all stakeholders take better decisionscustomized to characteristics of the datasets.

M (utt.) M (turn) M (dialog) Ubuntu 0.767 0.767 0.407 Insurance 0.7890.789 0.894 HR 0.801 0.803 0.423 Restaurant| 0.788 0.788 0.518

Exemplary Aspects, Using a Cloud Computing Environment

Although this detailed description includes an exemplary embodiment ofthe present invention in a cloud computing environment, it is to beunderstood that implementation of the teachings recited herein are notlimited to such a cloud computing environment. Rather, embodiments ofthe present invention are capable of being implemented in conjunctionwith any other type of computing environment now known or laterdeveloped.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client circuits through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablenode and is not intended to suggest any limitation as to the scope ofuse or functionality of embodiments of the invention described herein.Regardless, cloud computing node 10 is capable of being implementedand/or performing any of the functionality set forth herein.

Although cloud computing node 10 is depicted as a computer system/server12, it is understood to be operational with numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with computersystem/server 12 include, but are not limited to, personal computersystems, server computer systems, thin clients, thick clients, hand-heldor laptop circuits, multiprocessor systems, microprocessor-basedsystems, set top boxes, programmable consumer electronics, network PCs,minicomputer systems, mainframe computer systems, and distributed cloudcomputing environments that include any of the above systems orcircuits, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingcircuits that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage circuits.

Referring now to FIG. 5, a computer system/server 12 is shown in theform of a general-purpose computing circuit. The components of computersystem/server 12 may include, but are not limited to, one or moreprocessors or processing units 16, a system memory 28, and a bus 18 thatcouples various system components including system memory 28 toprocessor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further described below, memory 28 mayinclude a computer program product storing one or program modules 42comprising computer readable instructions configured to carry out one ormore features of the present invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may be adapted for implementation in anetworking environment. In some embodiments, program modules 42 areadapted to generally carry out one or more functions and/ormethodologies of the present invention.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing circuit, other peripherals,such as display 24, etc., and one or more components that facilitateinteraction with computer system/server 12. Such communication can occurvia Input/Output (I/O) interface 22, and/or any circuits (e.g., networkcard, modem, etc.) that enable computer system/server 12 to communicatewith one or more other computing circuits. For example, computersystem/server 12 can communicate with one or more networks such as alocal area network (LAN), a general wide area network (WAN), and/or apublic network (e.g., the Internet) via network adapter 20. As depicted,network adapter 20 communicates with the other components of computersystem/server 12 via bus 18. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system/server 12. Examples, include, but arenot limited to: microcode, circuit drivers, redundant processing units,external disk drive arrays, RAID systems, tape drives, and data archivalstorage systems, etc.

Referring now to FIG. 6, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing circuits used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingcircuit. It is understood that the types of computing circuits 54A-Nshown in FIG. 6 are intended to be illustrative only and that computingnodes 10 and cloud computing environment 50 can communicate with anytype of computerized circuit over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 7, an exemplary set of functional abstractionlayers provided by cloud computing environment 50 (FIG. 6) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 7 are intended to be illustrative only andembodiments of the invention are not limited thereto. As depicted, thefollowing layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage circuits 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of;cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and dialogue complexity assessment method 100in accordance with the present invention.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Further, Applicant's intent is to encompass the equivalents of all claimelements, and no amendment to any claim of the present applicationshould be construed as a disclaimer of any interest in or right to anequivalent of any element or feature of the amended claim.

What is claimed is:
 1. A computer-implemented dialogue complexityassessment method, the method comprising: calculating a complexity of adialogue using dialogue data from online repositories and contactcenters of service providers in stages by: computing a complexity of thedialogue at an utterance level; computing a complexity of the dialogueat a turn level; and computing a complexity of the dialogue based on thecomplexity of the constituent turns and utterances.
 2. Thecomputer-implemented method of claim 1, further comprising determining areason for the complexity of the dialogue, and wherein the complexity iscomputed by computing the complexity of the dialogue at the dialoguelevel based on the utterance level complexity and the turn levelcomplexity.
 3. The computer-implemented method of claim 2, wherein thedetermining further includes determining rules to explain a reason fordialogue complexity, the rules including a language of the dialogue, adomain of a conversation of the dialogue, and an understandability of acontent measured by their uniqueness to the domain.
 4. Thecomputer-implemented method of claim 1, further comprising managing aservice handling based on the calculated complexity of the dialogue. 5.The computer-implemented method of claim 4, wherein the managingdetermines how a service request is handled at each turn based on thecalculated complexity of the dialogue.
 6. The computer-implementedmethod of claim 1, further comprising ranking a set of service handlersbased on the calculated complexity of the dialogue.
 7. Thecomputer-implemented method of claim 1, wherein the complexity uses anN-gram structure.
 8. The computer-implemented method of claim 1, whereina customer rating of an interaction is weighted with the calculatedcomplexity of the dialogue and a duration of the dialogue, and averagedover a whole duration that an agent is to be evaluated during thedialogue to determine an agent score for a service handler.
 9. Thecomputer-implemented method of claim 6, wherein the service handlers aremanaged by at least one of: ranking a set of the service handlers;assigning an agent to handle service requests; ranking dialogues in acorpus based on the calculated complexity of the dialogue; and improvingan acquisition of a ground truth for a dialogue system.
 10. Thecomputer-implemented method of claim 1, embodied in a cloud-computingenvironment.
 11. A computer program product for dialogue complexityassessment, the computer program product comprising a computer readablestorage medium having program instructions embodied therewith, theprogram instructions executable by a computer to cause the computer toperform: calculating a complexity of a dialogue using dialogue data fromonline repositories and contact centers of service providers in stagesby: computing a complexity of the dialogue at an utterance level;computing a complexity of the dialogue at a turn level; and computing acomplexity of the dialogue based on the complexity of the constituentturns and utterances.
 12. The computer program product of claim 11,further comprising determining a reason for the complexity of thedialogue, and wherein the complexity is computed by computing thecomplexity of the dialogue at the dialogue level based on the utterancelevel complexity and the turn level complexity.
 13. The computer programproduct of claim 12, wherein the determining further includesdetermining rules to explain the reason for dialogue complexity, therules including a language of the dialogue, a domain of a conversationof the dialogue, and an understandability of a content measured by theiruniqueness to domain.
 14. The computer program product of claim 11,further comprising managing a service handling based on the calculatedcomplexity of the dialogue.
 15. The computer program product of claim14, wherein the managing determines how a service request is handled ateach turn based on the calculated complexity of the dialogue.
 16. Thecomputer program product of claim 11, further comprising ranking a setof service handlers based on the calculated complexity of the dialogue.17. The computer program product of claim 11, wherein the complexityuses an N-gram structure.
 18. A dialogue complexity assessment system,said system comprising: a processor; and a memory, the memory storinginstructions to cause the processor to perform: calculating a complexityof a dialogue using dialogue data from online repositories and contactcenters of service providers in stages by: computing a complexity of thedialogue at an utterance level; computing a complexity of the dialogueat a turn level; and computing a complexity of the dialogue based on thecomplexity of the constituent turns and utterances.
 19. The system ofclaim 18, embodied in a cloud-computing environment.
 20. Thecomputer-implemented method of claim 1, wherein the utterance level, theturn level, and the complexity of the dialogue are calculated anddirectly used as a dialogue corpus to automatically learn thedomain-dependent terms and the domain-independent terms and uses themalong with language terms and keywords for the complexity calculation.